Hi, everyone!I got a little free time around Christmas and decided to look into noise reduction.
There is a lot of research and open source code available, I ended up training a variation of
NAFnet architecture on astro images.
I called the result DeepSNR and it is available here:
http://deepsnrastro.com/Note that currently there is only PI plugin that works on RGB images. I totally forgot about Greyscale images as always, will try to add later.
It is deployed in the same exact way as StarNet, so installation and usage instructions are the same.
Note that there are two options to download: with and without Tensorflow libraries. If you have StarNet already you don't need to re-download and replace Tensorflow libs.
This is very fresh out the oven, I only did limited testing, so let me know how you find it working. Wei-Hao Wang was kind enough to test MacOS version, but the rest of the release materials are not really tested so far.
Also, it works on freshly integrated images with minimum postprocessing and is supposed to be applied early in the processing workflow.
Thanks!
Update: Based on the feedback I see that is works really well for images from monochrome cameras, but not suitable for anything that has been debayered prior to integration. It does not handle the low-frequency noise produced by that (the same goes for things like walking noise). This makes sense since I only trained it on images taken with monochrome cameras, so should have mentioned that from the start. Will work on the version that handles debayered images as well when I have time!
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